KIDNEY STONE DETECTION
USING IMAGE PROCESSING
Submitted By:
Mayank (05915603119)
Madhusudan Kaushik (07515603117)
Tushar Mawa (20215603119)
Akshat Jain (21015603119)
Contents
•What is image processing?
•Types of image processing
•Kidney stone detection: Need of the hour
•Role of image processing in kidney stone detection
•Use Case: Virtual Path Labs
•Code Flow
•Use Case Diagrams
•Results Glimpse
•References
What Is Image Processing?
•Image processing is the process of transforming an image into a
digital form and performing certain operations to get some useful
information from it. The image processing system usually treats all
images as 2D signals when applying certain predetermined signal
processing methods.
Types Of Image Processing
•There are five main types of image processing:
•Visualization - Find objects that are not visible in the image.
•Recognition - Distinguish or detect objects in the image.
•Sharpening and restoration - Create an enhanced image from the
original image.
•Pattern recognition - Measure the various patterns around the
objects in the image.
•Retrieval - Browse and search images from a large database of
digital images that are similar to the original image.
Kidney Stone Detection: Need Of The Hour
•Kidney stone disease is one of the risks for life throughout the
world and majority of people with stone formation in kidney at
the initial stage do not notice it as disease and it damages the
organ slowly. Current estimation is that there are 30 million
people suffering by this disease. There are different imaging
techniques for diagnosing kidney diseases, such as CT images, X-
rays, and Ultrasound imaging.
Role Of Image Processing In Kidney Stone
Detection
•Medical imaging plays a significant role in different clinical
applications such as medical procedures used for early detection,
monitoring, diagnosis, and treatment evaluation of various
medical conditions.
•In this project we are using one of the most important technique
of image processing called recognition. Image recognition is the
process of identifying an object or a feature in an image or video.
It is used in many applications like defect detection, medical
imaging, and security surveillance.
USER INTERFACE
Use Case: Virtual Path Labs
•What is Virtual Path Labs?
•It is a website that any user can use to detect stones in kidneys
using ultrasound or CT images.
•Simply the user has to upload a computer generated ultrasound or
CT image and it will show the detection results.
•It integrates MATLAB at its backend which is used to detect kidney
stones.
•By incorporating this technology in a website we ensure that it
becomes economical as the imaging centres will not require extra
equipment's to accomplish this.
Code Flow
•For accomplishing this project we are going to code in MATLAB using
MATLAB programming language.
•Firstly we will input the image we want to process by concatenating the
pathname and filename and using the imshow() function to display the
image.
•Then we will convert the image from rgb to gray scale using rgb2gray()
function and will be extracting the pixel information like at a coordinate
how many pixels are present and will use it to binarize the image using
binarize() function.
•Next we will filter the image and display only the pixels above a certain
threshold and filter all the unnecessary details in the image using
bwareopen() function.
•Next we will further pre-process the image by multiplying a
binary mask on an rgb image followed by contrast stretching.
•Next we will define the dimensions mostly between 0.3 and 0.7
using imadjust() function.
•Next we do the median filtering for the greyscale image using
the midfilt2() function. The following operations converges us
further to the desired result like gradient descent in linear
regression.
•And finally using the region of interest polynomial(roipoly)
create a mask/filter on the central(concerned) and check
whether binary object(stones) is present there or not.
USE CASE DIAGRAMS
RESULTS GLIMPSE
Input Image Output Image
Console Output
Input Image Output Image
Console Output
References
•https://www.hindawi.com/journals/vlsi/2015/581961/
•https://iopscience.iop.org/article/10.1088/2631-
8695/ac8b65/meta
•http://cse.anits.edu.in/projects/projects1920A4.pdf
•https://www.simplilearn.com/image-processing-article
•https://www.mathworks.com/discovery/image-recognition-
matlab.html